This code imports, cleans, and merges MODIS product data exported using the AppEEARS web application.
Exported MODIS data for the period 2000-2016:
- MOD11A2: LST Day 1km & LST Night 1km (8 day)
- MOD13Q1: EVI 250m & NDVI 250m (16 day)
- MOD15A2H: fPAR 500m & LAI 500m (8 day)
- MOD16A2: LE 500m & PET 500m (8 day)
- MOD17A2H: GPP 500m (8 day)
Data was exported for 50 BAMS paper sites (.csv format, “ID”, “Category”, “LAT”, “LONG”)
9 MODIS products are downloaded in 5 .csv files:
[1] "./BAMS-Sites-MOD11A2-006-results.csv" "./BAMS-Sites-MOD13Q1-006-results.csv"
[3] "./BAMS-Sites-MOD15A2H-006-results.csv" "./BAMS-Sites-MOD16A2-006-results.csv"
[5] "./BAMS-Sites-MOD17A2H-006-results.csv"
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
NA
Simplify data by removing unnecssary fields:
$lst_day
$vi
$lai
$et
$gpp
$lst_night
NA
Look at what the data quality levels are, convert good to 1, keep only 1s
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
Make a plot of daytime LST to make sure things look OK
ggplot(modis.filtered[[1]], aes(DATE, LST_D, col = as.factor(LAT))) +
geom_point()

Combine all the MODIS products for all sites and years into one dataframe
lst_d.filtered <- modis.filtered$lst_day %>% mutate(PRODUCT = rep("LST_D",length(LAT)), VAL = LST_D) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
lst_n.filtered <- modis.filtered$lst_night %>% mutate(PRODUCT = rep("LST_N",length(LAT)), VAL = LST_N) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
evi.filtered <- modis.filtered$vi %>% mutate(PRODUCT = rep("EVI",length(LAT)), VAL = EVI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
ndvi.filtered <- modis.filtered$vi %>% mutate(PRODUCT = rep("NDVI",length(LAT)), VAL = NDVI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
fpar.filtered <- modis.filtered$lai %>% mutate(PRODUCT = rep("FPAR",length(LAT)), VAL = FPAR) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
lai.filtered <- modis.filtered$lai %>% mutate(PRODUCT = rep("LAI",length(LAT)), VAL = LAI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
gpp.filtered <- modis.filtered$gpp %>% mutate(PRODUCT = rep("GPP",length(LAT)), VAL = GPP) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
le.filtered <- modis.filtered$et %>% mutate(PRODUCT = rep("LE",length(LAT)), VAL = LE) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
pet.filtered <- modis.filtered$et %>% mutate(PRODUCT = rep("PET",length(LAT)), VAL = PET) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
modis.filtered <- rbind(lst_d.filtered,lst_n.filtered,evi.filtered,ndvi.filtered,fpar.filtered,lai.filtered,
gpp.filtered,le.filtered,pet.filtered)
modis.filtered$PRODUCT <- as.factor(modis.filtered$PRODUCT)
str(modis.filtered)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 200018 obs. of 5 variables:
$ LAT : num 27.2 27.2 27.2 27.2 27.2 ...
$ LONG : num -81.2 -81.2 -81.2 -81.2 -81.2 ...
$ DATE : Date, format: "2000-03-13" "2000-03-21" "2000-03-29" ...
$ PRODUCT: Factor w/ 9 levels "EVI","FPAR","GPP",..: 6 6 6 6 6 6 6 6 6 6 ...
$ VAL : num 305 307 307 307 308 ...
Finally , join the observations to the Site List on the latitude column:
modis.full <- full_join(bams.sites, modis.filtered, by =c("LAT","LONG"))
str(modis.full)
'data.frame': 206698 obs. of 7 variables:
$ ID : Factor w/ 50 levels "BCBog","BCFEN",..: 21 21 21 21 21 21 21 21 21 21 ...
$ Category: Factor w/ 5 levels "CRO_OTHER","CRO_RICE",..: 5 5 5 5 5 5 5 5 5 5 ...
$ LAT : num 68.3 68.3 68.3 68.3 68.3 ...
$ LONG : num 19.1 19.1 19.1 19.1 19.1 ...
$ DATE : Date, format: "2000-04-06" "2000-05-16" "2000-07-03" ...
$ PRODUCT : Factor w/ 9 levels "EVI","FPAR","GPP",..: 6 6 6 6 6 6 6 6 6 6 ...
$ VAL : num 269 278 284 289 291 ...
Finished! Save as .csv
# set wd
setwd("C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data/bams-sites")
The working directory was changed to C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data/bams-sites inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
write.csv(modis.full, "bams_sites_MODIS_full.csv")
---
title: "Prepare MODIS Data"
output: html_notebook
---

This code imports, cleans, and merges MODIS product data exported using the [AppEEARS](https://lpdaacsvc.cr.usgs.gov/appeears) web application.

Exported MODIS data for the period 2000-2016:

* **MOD11A2:** LST Day 1km & LST Night 1km (8 day)
* **MOD13Q1:** EVI 250m & NDVI 250m (16 day)
* **MOD15A2H:** fPAR 500m & LAI 500m (8 day)
* **MOD16A2:** LE 500m & PET 500m (8 day)
* **MOD17A2H:** GPP 500m (8 day)

Data was exported for 50 BAMS paper sites (.csv format, "ID", "Category", "LAT", "LONG")
```{r echo=FALSE, warning=FALSE, message=FALSE}
# clear workspace and get wd
rm(list=ls())
# change wd
setwd("C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data/")
bams.sites <- read.csv("AppEEARS_sites.csv")
names(bams.sites)[c(3,4)] <- c("LAT", "LONG")
bams.sites
```


```{r echo=FALSE}
# libraries
# load tidyverse libraries
library(dplyr)
library(tidyr)
library(readr)
library(readxl)
library(stringr)
library(lubridate)
library(RColorBrewer)
library(ggplot2)
library(data.table)
library(magrittr)
library(caret)
library(caTools)
library(ranger)
library(tibble)
```

9 MODIS products are downloaded in 5 .csv files:
```{r message=FALSE, warning=FALSE, echo=FALSE}
# change wd
setwd("C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data/bams-sites")
# get site names from directory
site.names <- list.files(pattern = "\\.csv$",full.names=TRUE)
site.names
  # create list of products
modis <- list()
# create a function to read many csvs
read.many.csv <- function(i){
  read.csv(i, header=TRUE)
}
# execute function with site.names
modis <- lapply(site.names, read.many.csv)
modis
```
Simplify data by removing unnecssary fields:
```{r echo = FALSE}
lst.names <- names(modis[[1]][c(3,4,5,9,10,15,24)]) 
vi.names <- names(modis[[2]][c(3,4,5,9,10,14)])
lai.names <- names(modis[[3]][c(3,4,5,9,10,14)])
et.names <- names(modis[[4]][c(3,4,5,9,10,14)])
gpp.names <- names(modis[[5]][c(3,4,5,9,13)])
names.vec <- list(lst.names,vi.names, lai.names, et.names, gpp.names)
modis.simple <- list()
for (i in 1:length(modis)){
     modis.simple[[i]] <- as_tibble(modis[[i]][names.vec[[i]]])
     modis.simple[[i]]$Date <- as_date(modis.simple[[i]]$Date)
}
#split night and day LST; evi and NDVI; LAI and fPAR.
modis.simple[[6]] <- modis.simple[[1]] %>% select(Latitude, Longitude, Date, MOD11A2_006_LST_Night_1km, 
                                                  MOD11A2_006_QC_Night_MODLAND_Description)
modis.simple[[1]] <- modis.simple[[1]] %>% select(Latitude, Longitude, Date, MOD11A2_006_LST_Day_1km,
                                                  MOD11A2_006_QC_Day_MODLAND_Description)
names(modis.simple) <- c("lst_day","vi","lai","et","gpp","lst_night")
modis.simple
```
Look at what the data quality levels are, convert good to 1, keep only 1s
```{r echo= FALSE}
#convert levels to factor
levels(modis.simple[[1]][[5]]) <- as.factor(c(0,1,0))
levels(modis.simple[[2]][[6]]) <- as.factor(c(0,0,1,0))
levels(modis.simple[[3]][[6]]) <- as.factor(c(1,0))
levels(modis.simple[[4]][[6]]) <- as.factor(c(1,0))
levels(modis.simple[[5]][[5]]) <- as.factor(c(1,0))
levels(modis.simple[[6]][[5]]) <- as.factor(c(0,1,0))
#rename variables
names(modis.simple[[1]]) <- c("LAT", "LONG", "DATE", "LST_D", "QUALITY")
names(modis.simple[[2]]) <- c("LAT", "LONG", "DATE", "EVI", "NDVI", "QUALITY")
names(modis.simple[[3]]) <- c("LAT", "LONG", "DATE", "FPAR", "LAI", "QUALITY")
names(modis.simple[[4]]) <- c("LAT", "LONG", "DATE", "LE", "PET", "QUALITY")
names(modis.simple[[5]]) <- c("LAT", "LONG", "DATE", "GPP", "QUALITY")
names(modis.simple[[6]]) <- c("LAT", "LONG", "DATE", "LST_N", "QUALITY")
# modis.simple

modis.filtered <- list()
for (i in 1:length(modis.simple)){
  modis.filtered[[i]] <- as_tibble(modis.simple[[i]] %>% filter(QUALITY == 1))
}
modis.filtered
names(modis.filtered) <- c("lst_day","vi","lai","et","gpp","lst_night")
```
Make a plot of daytime LST to make sure things look OK
```{r}
ggplot(modis.filtered[[1]], aes(DATE, LST_D, col = as.factor(LAT))) +
  geom_point()
```
Combine all the MODIS products for all sites and years into one dataframe
```{r}
lst_d.filtered <- modis.filtered$lst_day %>% mutate(PRODUCT = rep("LST_D",length(LAT)), VAL = LST_D) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
lst_n.filtered <- modis.filtered$lst_night %>% mutate(PRODUCT = rep("LST_N",length(LAT)), VAL = LST_N) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
evi.filtered <- modis.filtered$vi %>% mutate(PRODUCT = rep("EVI",length(LAT)), VAL = EVI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
ndvi.filtered <- modis.filtered$vi  %>% mutate(PRODUCT = rep("NDVI",length(LAT)), VAL = NDVI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
fpar.filtered <- modis.filtered$lai  %>% mutate(PRODUCT = rep("FPAR",length(LAT)), VAL = FPAR) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
lai.filtered <- modis.filtered$lai  %>% mutate(PRODUCT = rep("LAI",length(LAT)), VAL = LAI) %>% select(LAT, LONG, DATE, PRODUCT, VAL)
gpp.filtered <- modis.filtered$gpp %>% mutate(PRODUCT = rep("GPP",length(LAT)), VAL = GPP) %>% select(LAT, LONG, DATE, PRODUCT, VAL) 
le.filtered <- modis.filtered$et %>% mutate(PRODUCT = rep("LE",length(LAT)), VAL = LE) %>%  select(LAT, LONG, DATE, PRODUCT, VAL) 
pet.filtered <- modis.filtered$et %>% mutate(PRODUCT = rep("PET",length(LAT)), VAL = PET) %>% select(LAT, LONG, DATE, PRODUCT, VAL) 

modis.filtered <- rbind(lst_d.filtered,lst_n.filtered,evi.filtered,ndvi.filtered,fpar.filtered,lai.filtered,
                        gpp.filtered,le.filtered,pet.filtered)
modis.filtered$PRODUCT <- as.factor(modis.filtered$PRODUCT)
str(modis.filtered)

```
Finally , join the observations to the Site List on the latitude column:
```{r}
modis.full <- full_join(bams.sites, modis.filtered, by =c("LAT","LONG"))
str(modis.full)
```
Finished! Save as .csv
```{r warning=FALSE, message=FALSE}
# set wd
setwd("C:/Users/Gavin McNicol/Box Sync/MODIS Data/AppEEARS Data/bams-sites")
write.csv(modis.full, "bams_sites_MODIS_full.csv")
```




